R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(9
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+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('Variable'
+ ,'Parameter'
+ ,'S.D.'
+ ,'T-STAT'
+ ,'2-tail'
+ ,'1-tail'
+ ,'MultipleLinearRegression')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('Variable','Parameter','S.D.','T-STAT','2-tail','1-tail','MultipleLinearRegression'),1:159))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Parameter Variable S.D. T-STAT 2-tail 1-tail MultipleLinearRegression
1 26 9 24 14 11 12.0 24
2 23 9 25 11 7 8.0 25
3 25 9 17 6 17 8.0 30
4 23 9 18 12 10 8.0 19
5 19 9 18 8 12 9.0 22
6 29 9 16 10 12 7.0 22
7 25 10 20 10 11 4.0 25
8 21 10 16 11 11 11.0 23
9 22 10 18 16 12 7.0 17
10 25 10 17 11 13 7.0 21
11 24 10 23 13 14 12.0 19
12 18 10 30 12 16 10.0 19
13 22 10 23 8 11 10.0 15
14 15 10 18 12 10 8.0 16
15 22 10 15 11 11 7.9 23
16 28 10 12 4 15 4.0 27
17 20 10 21 9 9 9.0 22
18 12 10 15 8 11 8.0 14
19 24 10 20 8 17 7.0 22
20 20 10 31 14 17 11.0 23
21 21 10 27 15 11 9.0 23
22 20 10 34 16 18 11.0 21
23 21 10 21 9 14 13.0 19
24 23 10 31 14 10 8.0 18
25 28 10 19 11 11 8.0 20
26 24 10 16 8 15 9.0 23
27 24 10 20 9 15 6.0 25
28 24 10 21 9 13 9.0 19
29 23 10 22 9 16 9.0 24
30 23 10 17 9 13 6.0 22
31 29 10 24 10 9 6.0 25
32 24 10 25 16 18 16.0 26
33 18 10 26 11 18 5.0 29
34 25 10 25 8 12 7.0 32
35 21 10 17 9 17 9.0 25
36 26 10 32 16 9 6.0 29
37 22 10 33 11 9 6.0 28
38 22 10 13 16 12 5.0 17
39 22 10 32 12 18 12.0 28
40 23 10 25 12 12 7.0 29
41 30 10 29 14 18 10.0 26
42 23 10 22 9 14 9.0 25
43 17 10 18 10 15 8.0 14
44 23 10 17 9 16 5.0 25
45 23 10 20 10 10 8.0 26
46 25 10 15 12 11 8.0 20
47 24 10 20 14 14 10.0 18
48 24 10 33 14 9 6.0 32
49 23 10 29 10 12 8.0 25
50 21 10 23 14 17 7.0 25
51 24 10 26 16 5 4.0 23
52 24 10 18 9 12 8.0 21
53 28 10 20 10 12 8.0 20
54 16 10 11 6 6 4.0 15
55 20 10 28 8 24 20.0 30
56 29 10 26 13 12 8.0 24
57 27 10 22 10 12 8.0 26
58 22 10 17 8 14 6.0 24
59 28 10 12 7 7 4.0 22
60 16 10 14 15 13 8.0 14
61 25 10 17 9 12 9.0 24
62 24 10 21 10 13 6.0 24
63 28 10 19 12 14 7.0 24
64 24 10 18 13 8 9.0 24
65 23 10 10 10 11 5.0 19
66 30 10 29 11 9 5.0 31
67 24 10 31 8 11 8.0 22
68 21 10 19 9 13 8.0 27
69 25 10 9 13 10 6.0 19
70 25 10 20 11 11 8.0 25
71 22 10 28 8 12 7.0 20
72 23 10 19 9 9 7.0 21
73 26 10 30 9 15 9.0 27
74 23 10 29 15 18 11.0 23
75 25 10 26 9 15 6.0 25
76 21 10 23 10 12 8.0 20
77 25 10 13 14 13 6.0 21
78 24 10 21 12 14 9.0 22
79 29 10 19 12 10 8.0 23
80 22 10 28 11 13 6.0 25
81 27 10 23 14 13 10.0 25
82 26 10 18 6 11 8.0 17
83 22 10 21 12 13 8.0 19
84 24 10 20 8 16 10.0 25
85 27 10 23 14 8 5.0 19
86 24 10 21 11 16 7.0 20
87 24 10 21 10 11 5.0 26
88 29 10 15 14 9 8.0 23
89 22 10 28 12 16 14.0 27
90 21 10 19 10 12 7.0 17
91 24 10 26 14 14 8.0 17
92 24 10 10 5 8 6.0 19
93 23 10 16 11 9 5.0 17
94 20 10 22 10 15 6.0 22
95 27 10 19 9 11 10.0 21
96 26 10 31 10 21 12.0 32
97 25 10 31 16 14 9.0 21
98 21 10 29 13 18 12.0 21
99 21 10 19 9 12 7.0 18
100 19 10 22 10 13 8.0 18
101 21 10 23 10 15 10.0 23
102 21 10 15 7 12 6.0 19
103 16 10 20 9 19 10.0 20
104 22 10 18 8 15 10.0 21
105 29 10 23 14 11 10.0 20
106 15 10 25 14 11 5.0 17
107 17 10 21 8 10 7.0 18
108 15 10 24 9 13 10.0 19
109 21 10 25 14 15 11.0 22
110 21 10 17 14 12 6.0 15
111 19 10 13 8 12 7.0 14
112 24 10 28 8 16 12.0 18
113 20 10 21 8 9 11.0 24
114 17 10 25 7 18 11.0 35
115 23 10 9 6 8 11.0 29
116 24 10 16 8 13 5.0 21
117 14 10 19 6 17 8.0 25
118 19 10 17 11 9 6.0 20
119 24 10 25 14 15 9.0 22
120 13 10 20 11 8 4.0 13
121 22 10 29 11 7 4.0 26
122 16 10 14 11 12 7.0 17
123 19 10 22 14 14 11.0 25
124 25 10 15 8 6 6.0 20
125 25 10 19 20 8 7.0 19
126 23 10 20 11 17 8.0 21
127 24 10 15 8 10 4.0 22
128 26 10 20 11 11 8.0 24
129 26 10 18 10 14 9.0 21
130 25 10 33 14 11 8.0 26
131 18 10 22 11 13 11.0 24
132 21 10 16 9 12 8.0 16
133 26 10 17 9 11 5.0 23
134 23 10 16 8 9 4.0 18
135 23 10 21 10 12 8.0 16
136 22 10 26 13 20 10.0 26
137 20 10 18 13 12 6.0 19
138 13 10 18 12 13 9.0 21
139 24 10 17 8 12 9.0 21
140 15 10 22 13 12 13.0 22
141 14 10 30 14 9 9.0 23
142 22 10 30 12 15 10.0 29
143 10 10 24 14 24 20.0 21
144 24 10 21 15 7 5.0 21
145 22 10 21 13 17 11.0 23
146 24 10 29 16 11 6.0 27
147 19 10 31 9 17 9.0 25
148 20 10 20 9 11 7.0 21
149 13 10 16 9 12 9.0 10
150 20 10 22 8 14 10.0 20
151 22 10 20 7 11 9.0 26
152 24 10 28 16 16 8.0 24
153 29 10 38 11 21 7.0 29
154 12 10 22 9 14 6.0 19
155 20 10 20 11 20 13.0 24
156 21 10 17 9 13 6.0 19
157 24 10 28 14 11 8.0 24
158 22 10 22 13 15 10.0 22
159 20 10 31 16 19 16.0 17
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Variable S.D.
28.21131 -1.21076 -0.06624
`T-STAT` `2-tail` `1-tail`
0.21998 -0.13804 -0.26768
MultipleLinearRegression
0.41518
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.0937 -1.7734 0.2311 2.2693 7.2319
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 28.21131 14.94576 1.888 0.0610 .
Variable -1.21076 1.48482 -0.815 0.4161
S.D. -0.06624 0.06322 -1.048 0.2964
`T-STAT` 0.21998 0.11277 1.951 0.0529 .
`2-tail` -0.13804 0.10526 -1.311 0.1917
`1-tail` -0.26768 0.13150 -2.036 0.0435 *
MultipleLinearRegression 0.41518 0.07626 5.444 2.05e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.503 on 152 degrees of freedom
Multiple R-squared: 0.2257, Adjusted R-squared: 0.1952
F-statistic: 7.386 on 6 and 152 DF, p-value: 6.016e-07
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.527011339 0.945977321 0.4729887
[2,] 0.562050758 0.875898483 0.4379492
[3,] 0.488797048 0.977594096 0.5112030
[4,] 0.540312609 0.919374782 0.4596874
[5,] 0.741606301 0.516787397 0.2583937
[6,] 0.654975316 0.690049367 0.3450247
[7,] 0.624349814 0.751300372 0.3756502
[8,] 0.538687107 0.922625787 0.4613129
[9,] 0.676523850 0.646952300 0.3234762
[10,] 0.601787714 0.796424571 0.3982123
[11,] 0.589009300 0.821981400 0.4109907
[12,] 0.525027626 0.949944748 0.4749724
[13,] 0.455644610 0.911289221 0.5443554
[14,] 0.404802514 0.809605029 0.5951975
[15,] 0.409292696 0.818585392 0.5907073
[16,] 0.571837158 0.856325685 0.4281628
[17,] 0.510576212 0.978847576 0.4894238
[18,] 0.443519563 0.887039125 0.5564804
[19,] 0.437932035 0.875864071 0.5620680
[20,] 0.373771832 0.747543663 0.6262282
[21,] 0.313336026 0.626672052 0.6866640
[22,] 0.347263297 0.694526595 0.6527367
[23,] 0.296163154 0.592326307 0.7038368
[24,] 0.521927365 0.956145269 0.4780726
[25,] 0.470812922 0.941625844 0.5291871
[26,] 0.432862899 0.865725799 0.5671371
[27,] 0.375546236 0.751092473 0.6244538
[28,] 0.348910287 0.697820575 0.6510897
[29,] 0.297272497 0.594544994 0.7027275
[30,] 0.249904029 0.499808057 0.7500960
[31,] 0.222560825 0.445121651 0.7774392
[32,] 0.375203931 0.750407863 0.6247961
[33,] 0.323419485 0.646838971 0.6765805
[34,] 0.291372185 0.582744370 0.7086278
[35,] 0.247786889 0.495573778 0.7522131
[36,] 0.211393905 0.422787810 0.7886061
[37,] 0.191987018 0.383974036 0.8080130
[38,] 0.179547882 0.359095765 0.8204521
[39,] 0.164459893 0.328919785 0.8355401
[40,] 0.134369240 0.268738480 0.8656308
[41,] 0.124728456 0.249456913 0.8752715
[42,] 0.102954440 0.205908880 0.8970456
[43,] 0.087832970 0.175665940 0.9121670
[44,] 0.147392748 0.294785495 0.8526073
[45,] 0.176759866 0.353519732 0.8232401
[46,] 0.165314067 0.330628133 0.8346859
[47,] 0.222746328 0.445492656 0.7772537
[48,] 0.215184325 0.430368650 0.7848157
[49,] 0.184195411 0.368390821 0.8158046
[50,] 0.197396036 0.394792072 0.8026040
[51,] 0.225127634 0.450255269 0.7748724
[52,] 0.198905634 0.397811268 0.8010944
[53,] 0.167144064 0.334288127 0.8328559
[54,] 0.182384014 0.364768028 0.8176160
[55,] 0.153297484 0.306594967 0.8467025
[56,] 0.126476445 0.252952889 0.8735236
[57,] 0.122928310 0.245856620 0.8770717
[58,] 0.112290924 0.224581848 0.8877091
[59,] 0.111389709 0.222779417 0.8886103
[60,] 0.094797851 0.189595701 0.9052021
[61,] 0.077395403 0.154790807 0.9226046
[62,] 0.062899220 0.125798440 0.9371008
[63,] 0.049682013 0.099364027 0.9503180
[64,] 0.046640046 0.093280093 0.9533600
[65,] 0.037362175 0.074724350 0.9626378
[66,] 0.031051733 0.062103466 0.9689483
[67,] 0.023817202 0.047634403 0.9761828
[68,] 0.018700231 0.037400461 0.9812998
[69,] 0.014968940 0.029937881 0.9850311
[70,] 0.022492401 0.044984801 0.9775076
[71,] 0.017931427 0.035862854 0.9820686
[72,] 0.017533894 0.035067788 0.9824661
[73,] 0.031361480 0.062722961 0.9686385
[74,] 0.024186212 0.048372424 0.9758138
[75,] 0.020179899 0.040359797 0.9798201
[76,] 0.022102080 0.044204161 0.9778979
[77,] 0.019589696 0.039179393 0.9804103
[78,] 0.014892082 0.029784165 0.9851079
[79,] 0.019443287 0.038886575 0.9805567
[80,] 0.015290831 0.030581662 0.9847092
[81,] 0.011441891 0.022883781 0.9885581
[82,] 0.011501518 0.023003037 0.9884985
[83,] 0.010055661 0.020111321 0.9899443
[84,] 0.007711865 0.015423730 0.9922881
[85,] 0.006374597 0.012749193 0.9936254
[86,] 0.011679987 0.023359975 0.9883200
[87,] 0.010578120 0.021156240 0.9894219
[88,] 0.010061256 0.020122513 0.9899387
[89,] 0.007699113 0.015398226 0.9923009
[90,] 0.005680825 0.011361650 0.9943192
[91,] 0.004348979 0.008697957 0.9956510
[92,] 0.003213483 0.006426966 0.9967865
[93,] 0.002286472 0.004572945 0.9977135
[94,] 0.002444720 0.004889440 0.9975553
[95,] 0.001915245 0.003830490 0.9980848
[96,] 0.008114481 0.016228963 0.9918855
[97,] 0.018161872 0.036323744 0.9818381
[98,] 0.018024504 0.036049007 0.9819755
[99,] 0.022717948 0.045435896 0.9772821
[100,] 0.017527722 0.035055444 0.9824723
[101,] 0.012802049 0.025604098 0.9871980
[102,] 0.009268614 0.018537228 0.9907314
[103,] 0.022728373 0.045456747 0.9772716
[104,] 0.020597181 0.041194362 0.9794028
[105,] 0.057200294 0.114400588 0.9427997
[106,] 0.048462705 0.096925410 0.9515373
[107,] 0.039276437 0.078552874 0.9607236
[108,] 0.115731467 0.231462934 0.8842685
[109,] 0.110887113 0.221774227 0.8891129
[110,] 0.098312791 0.196625582 0.9016872
[111,] 0.171833931 0.343667861 0.8281661
[112,] 0.163712493 0.327424987 0.8362875
[113,] 0.195066967 0.390133934 0.8049330
[114,] 0.188054053 0.376108106 0.8119459
[115,] 0.187235425 0.374470850 0.8127646
[116,] 0.170002447 0.340004894 0.8299976
[117,] 0.138341399 0.276682798 0.8616586
[118,] 0.108178568 0.216357136 0.8918214
[119,] 0.111630465 0.223260930 0.8883695
[120,] 0.160133501 0.320267002 0.8398665
[121,] 0.137951852 0.275903704 0.8620481
[122,] 0.120423104 0.240846209 0.8795769
[123,] 0.104299008 0.208598016 0.8957010
[124,] 0.095674176 0.191348352 0.9043258
[125,] 0.078100593 0.156201185 0.9218994
[126,] 0.106580435 0.213160870 0.8934196
[127,] 0.079028732 0.158057464 0.9209713
[128,] 0.057635798 0.115271597 0.9423642
[129,] 0.146870834 0.293741668 0.8531292
[130,] 0.208134150 0.416268300 0.7918659
[131,] 0.188534490 0.377068979 0.8114655
[132,] 0.406055846 0.812111691 0.5939442
[133,] 0.356404340 0.712808681 0.6435957
[134,] 0.666036976 0.667926049 0.3339630
[135,] 0.604830923 0.790338153 0.3951691
[136,] 0.494206523 0.988413046 0.5057935
[137,] 0.423316958 0.846633916 0.5766830
[138,] 0.433611034 0.867222068 0.5663890
[139,] 0.303307218 0.606614436 0.6966928
[140,] 0.211421302 0.422842605 0.7885787
> postscript(file="/var/www/html/rcomp/tmp/1254s1290537787.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2delv1290537787.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3delv1290537787.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4delv1290537787.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5delv1290537787.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 159
Frequency = 1
1 2 3 4 5 6
1.96198836 -2.34990302 -0.47541873 -0.12838779 -3.95022967 4.94195085
7 8 9 10 11 12
0.23105658 -1.54976023 0.04120504 2.55219581 3.81651422 -1.75907577
13 14 15 16 17 18
3.62765168 -5.67208736 -1.44582232 3.74280788 -2.17484625 -7.02248808
19 20 21 22 23 24
2.54786551 -1.38779014 -2.23637049 -0.66062022 1.83163864 1.91876212
25 26 27 28 29 30
6.09146220 2.12699047 0.53857534 3.62286332 1.02733562 0.30929750
31 32 33 34 35 36
4.75531461 2.00569913 -7.01819970 -0.96292485 -1.58102251 -0.69534591
37 38 39 40 41 42
-3.11401000 -0.82538213 -0.55175486 -2.59731477 7.10454125 0.33607077
43 44 45 46 47 48
-1.71155041 -0.78979783 -1.25143295 2.60650322 3.27761284 -3.43467660
49 50 51 52 53 54
0.03602926 -2.81883590 -1.68926801 2.18804543 6.51573122 -5.02362508
55 56 57 58 59 60
0.20255893 5.59252953 3.15714033 -1.16303736 4.05442013 -4.35252389
61 62 63 64 65 66
2.14394489 0.52393199 4.35720463 -0.22191044 0.32737746 3.10779059
67 68 69 70 71 72
2.71597942 -3.09874711 1.73082689 1.08180703 1.21796598 0.57247890
73 74 75 76 77 78
3.17370674 1.39778167 1.93604025 -0.28553632 1.35958887 1.85541908
79 80 81 82 83 84
5.48789773 -1.64752119 3.43204374 6.37066954 0.69523298 1.96733304
85 86 87 88 89 90
3.89449444 2.64647976 -0.85019593 4.64491388 -0.14227017 0.42734346
91 92 93 94 95 96
3.55489120 2.28084508 1.05913474 -2.30337914 5.65161350 2.57537377
97 98 99 100 101 102
3.05311094 0.93578937 0.23214597 -1.38337825 -0.58158203 -0.27572895
103 104 105 106 107 108
-3.76262253 1.35752171 7.23185808 -6.72853005 -3.69146833 -4.91072102
109 110 111 112 113 114
-0.64616017 -0.02239783 -0.28461709 5.93891172 -2.24985719 -8.08949429
115 116 117 118 119 120
-1.81876407 1.61053238 -8.05627027 -3.85247750 1.81847342 -7.42089490
121 122 123 124 125 126
-3.36007828 -5.12385967 -4.22847471 2.26085385 0.84498997 1.57078145
127 128 129 130 131 132
0.44729768 2.49698689 4.51183125 0.86785399 -4.29139020 1.13145646
133 134 135 136 137 138
2.35034944 1.03621880 3.24269484 -0.59812384 -2.39689076 -9.06617600
139 140 141 142 143 144
2.60946687 -6.50367131 -9.09374065 -2.04891692 -7.64569315 -0.42637847
145 146 147 148 149 150
0.16975073 -1.78763365 -2.65360439 -2.08519196 -4.10978114 -0.10036432
151 152 153 154 155 156
-1.18576012 0.61724067 5.72622401 -8.97589966 -0.92221464 -0.44516291
157 158 159
0.36699296 0.10740655 2.27782532
> postscript(file="/var/www/html/rcomp/tmp/656ky1290537787.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 1.96198836 NA
1 -2.34990302 1.96198836
2 -0.47541873 -2.34990302
3 -0.12838779 -0.47541873
4 -3.95022967 -0.12838779
5 4.94195085 -3.95022967
6 0.23105658 4.94195085
7 -1.54976023 0.23105658
8 0.04120504 -1.54976023
9 2.55219581 0.04120504
10 3.81651422 2.55219581
11 -1.75907577 3.81651422
12 3.62765168 -1.75907577
13 -5.67208736 3.62765168
14 -1.44582232 -5.67208736
15 3.74280788 -1.44582232
16 -2.17484625 3.74280788
17 -7.02248808 -2.17484625
18 2.54786551 -7.02248808
19 -1.38779014 2.54786551
20 -2.23637049 -1.38779014
21 -0.66062022 -2.23637049
22 1.83163864 -0.66062022
23 1.91876212 1.83163864
24 6.09146220 1.91876212
25 2.12699047 6.09146220
26 0.53857534 2.12699047
27 3.62286332 0.53857534
28 1.02733562 3.62286332
29 0.30929750 1.02733562
30 4.75531461 0.30929750
31 2.00569913 4.75531461
32 -7.01819970 2.00569913
33 -0.96292485 -7.01819970
34 -1.58102251 -0.96292485
35 -0.69534591 -1.58102251
36 -3.11401000 -0.69534591
37 -0.82538213 -3.11401000
38 -0.55175486 -0.82538213
39 -2.59731477 -0.55175486
40 7.10454125 -2.59731477
41 0.33607077 7.10454125
42 -1.71155041 0.33607077
43 -0.78979783 -1.71155041
44 -1.25143295 -0.78979783
45 2.60650322 -1.25143295
46 3.27761284 2.60650322
47 -3.43467660 3.27761284
48 0.03602926 -3.43467660
49 -2.81883590 0.03602926
50 -1.68926801 -2.81883590
51 2.18804543 -1.68926801
52 6.51573122 2.18804543
53 -5.02362508 6.51573122
54 0.20255893 -5.02362508
55 5.59252953 0.20255893
56 3.15714033 5.59252953
57 -1.16303736 3.15714033
58 4.05442013 -1.16303736
59 -4.35252389 4.05442013
60 2.14394489 -4.35252389
61 0.52393199 2.14394489
62 4.35720463 0.52393199
63 -0.22191044 4.35720463
64 0.32737746 -0.22191044
65 3.10779059 0.32737746
66 2.71597942 3.10779059
67 -3.09874711 2.71597942
68 1.73082689 -3.09874711
69 1.08180703 1.73082689
70 1.21796598 1.08180703
71 0.57247890 1.21796598
72 3.17370674 0.57247890
73 1.39778167 3.17370674
74 1.93604025 1.39778167
75 -0.28553632 1.93604025
76 1.35958887 -0.28553632
77 1.85541908 1.35958887
78 5.48789773 1.85541908
79 -1.64752119 5.48789773
80 3.43204374 -1.64752119
81 6.37066954 3.43204374
82 0.69523298 6.37066954
83 1.96733304 0.69523298
84 3.89449444 1.96733304
85 2.64647976 3.89449444
86 -0.85019593 2.64647976
87 4.64491388 -0.85019593
88 -0.14227017 4.64491388
89 0.42734346 -0.14227017
90 3.55489120 0.42734346
91 2.28084508 3.55489120
92 1.05913474 2.28084508
93 -2.30337914 1.05913474
94 5.65161350 -2.30337914
95 2.57537377 5.65161350
96 3.05311094 2.57537377
97 0.93578937 3.05311094
98 0.23214597 0.93578937
99 -1.38337825 0.23214597
100 -0.58158203 -1.38337825
101 -0.27572895 -0.58158203
102 -3.76262253 -0.27572895
103 1.35752171 -3.76262253
104 7.23185808 1.35752171
105 -6.72853005 7.23185808
106 -3.69146833 -6.72853005
107 -4.91072102 -3.69146833
108 -0.64616017 -4.91072102
109 -0.02239783 -0.64616017
110 -0.28461709 -0.02239783
111 5.93891172 -0.28461709
112 -2.24985719 5.93891172
113 -8.08949429 -2.24985719
114 -1.81876407 -8.08949429
115 1.61053238 -1.81876407
116 -8.05627027 1.61053238
117 -3.85247750 -8.05627027
118 1.81847342 -3.85247750
119 -7.42089490 1.81847342
120 -3.36007828 -7.42089490
121 -5.12385967 -3.36007828
122 -4.22847471 -5.12385967
123 2.26085385 -4.22847471
124 0.84498997 2.26085385
125 1.57078145 0.84498997
126 0.44729768 1.57078145
127 2.49698689 0.44729768
128 4.51183125 2.49698689
129 0.86785399 4.51183125
130 -4.29139020 0.86785399
131 1.13145646 -4.29139020
132 2.35034944 1.13145646
133 1.03621880 2.35034944
134 3.24269484 1.03621880
135 -0.59812384 3.24269484
136 -2.39689076 -0.59812384
137 -9.06617600 -2.39689076
138 2.60946687 -9.06617600
139 -6.50367131 2.60946687
140 -9.09374065 -6.50367131
141 -2.04891692 -9.09374065
142 -7.64569315 -2.04891692
143 -0.42637847 -7.64569315
144 0.16975073 -0.42637847
145 -1.78763365 0.16975073
146 -2.65360439 -1.78763365
147 -2.08519196 -2.65360439
148 -4.10978114 -2.08519196
149 -0.10036432 -4.10978114
150 -1.18576012 -0.10036432
151 0.61724067 -1.18576012
152 5.72622401 0.61724067
153 -8.97589966 5.72622401
154 -0.92221464 -8.97589966
155 -0.44516291 -0.92221464
156 0.36699296 -0.44516291
157 0.10740655 0.36699296
158 2.27782532 0.10740655
159 NA 2.27782532
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.34990302 1.96198836
[2,] -0.47541873 -2.34990302
[3,] -0.12838779 -0.47541873
[4,] -3.95022967 -0.12838779
[5,] 4.94195085 -3.95022967
[6,] 0.23105658 4.94195085
[7,] -1.54976023 0.23105658
[8,] 0.04120504 -1.54976023
[9,] 2.55219581 0.04120504
[10,] 3.81651422 2.55219581
[11,] -1.75907577 3.81651422
[12,] 3.62765168 -1.75907577
[13,] -5.67208736 3.62765168
[14,] -1.44582232 -5.67208736
[15,] 3.74280788 -1.44582232
[16,] -2.17484625 3.74280788
[17,] -7.02248808 -2.17484625
[18,] 2.54786551 -7.02248808
[19,] -1.38779014 2.54786551
[20,] -2.23637049 -1.38779014
[21,] -0.66062022 -2.23637049
[22,] 1.83163864 -0.66062022
[23,] 1.91876212 1.83163864
[24,] 6.09146220 1.91876212
[25,] 2.12699047 6.09146220
[26,] 0.53857534 2.12699047
[27,] 3.62286332 0.53857534
[28,] 1.02733562 3.62286332
[29,] 0.30929750 1.02733562
[30,] 4.75531461 0.30929750
[31,] 2.00569913 4.75531461
[32,] -7.01819970 2.00569913
[33,] -0.96292485 -7.01819970
[34,] -1.58102251 -0.96292485
[35,] -0.69534591 -1.58102251
[36,] -3.11401000 -0.69534591
[37,] -0.82538213 -3.11401000
[38,] -0.55175486 -0.82538213
[39,] -2.59731477 -0.55175486
[40,] 7.10454125 -2.59731477
[41,] 0.33607077 7.10454125
[42,] -1.71155041 0.33607077
[43,] -0.78979783 -1.71155041
[44,] -1.25143295 -0.78979783
[45,] 2.60650322 -1.25143295
[46,] 3.27761284 2.60650322
[47,] -3.43467660 3.27761284
[48,] 0.03602926 -3.43467660
[49,] -2.81883590 0.03602926
[50,] -1.68926801 -2.81883590
[51,] 2.18804543 -1.68926801
[52,] 6.51573122 2.18804543
[53,] -5.02362508 6.51573122
[54,] 0.20255893 -5.02362508
[55,] 5.59252953 0.20255893
[56,] 3.15714033 5.59252953
[57,] -1.16303736 3.15714033
[58,] 4.05442013 -1.16303736
[59,] -4.35252389 4.05442013
[60,] 2.14394489 -4.35252389
[61,] 0.52393199 2.14394489
[62,] 4.35720463 0.52393199
[63,] -0.22191044 4.35720463
[64,] 0.32737746 -0.22191044
[65,] 3.10779059 0.32737746
[66,] 2.71597942 3.10779059
[67,] -3.09874711 2.71597942
[68,] 1.73082689 -3.09874711
[69,] 1.08180703 1.73082689
[70,] 1.21796598 1.08180703
[71,] 0.57247890 1.21796598
[72,] 3.17370674 0.57247890
[73,] 1.39778167 3.17370674
[74,] 1.93604025 1.39778167
[75,] -0.28553632 1.93604025
[76,] 1.35958887 -0.28553632
[77,] 1.85541908 1.35958887
[78,] 5.48789773 1.85541908
[79,] -1.64752119 5.48789773
[80,] 3.43204374 -1.64752119
[81,] 6.37066954 3.43204374
[82,] 0.69523298 6.37066954
[83,] 1.96733304 0.69523298
[84,] 3.89449444 1.96733304
[85,] 2.64647976 3.89449444
[86,] -0.85019593 2.64647976
[87,] 4.64491388 -0.85019593
[88,] -0.14227017 4.64491388
[89,] 0.42734346 -0.14227017
[90,] 3.55489120 0.42734346
[91,] 2.28084508 3.55489120
[92,] 1.05913474 2.28084508
[93,] -2.30337914 1.05913474
[94,] 5.65161350 -2.30337914
[95,] 2.57537377 5.65161350
[96,] 3.05311094 2.57537377
[97,] 0.93578937 3.05311094
[98,] 0.23214597 0.93578937
[99,] -1.38337825 0.23214597
[100,] -0.58158203 -1.38337825
[101,] -0.27572895 -0.58158203
[102,] -3.76262253 -0.27572895
[103,] 1.35752171 -3.76262253
[104,] 7.23185808 1.35752171
[105,] -6.72853005 7.23185808
[106,] -3.69146833 -6.72853005
[107,] -4.91072102 -3.69146833
[108,] -0.64616017 -4.91072102
[109,] -0.02239783 -0.64616017
[110,] -0.28461709 -0.02239783
[111,] 5.93891172 -0.28461709
[112,] -2.24985719 5.93891172
[113,] -8.08949429 -2.24985719
[114,] -1.81876407 -8.08949429
[115,] 1.61053238 -1.81876407
[116,] -8.05627027 1.61053238
[117,] -3.85247750 -8.05627027
[118,] 1.81847342 -3.85247750
[119,] -7.42089490 1.81847342
[120,] -3.36007828 -7.42089490
[121,] -5.12385967 -3.36007828
[122,] -4.22847471 -5.12385967
[123,] 2.26085385 -4.22847471
[124,] 0.84498997 2.26085385
[125,] 1.57078145 0.84498997
[126,] 0.44729768 1.57078145
[127,] 2.49698689 0.44729768
[128,] 4.51183125 2.49698689
[129,] 0.86785399 4.51183125
[130,] -4.29139020 0.86785399
[131,] 1.13145646 -4.29139020
[132,] 2.35034944 1.13145646
[133,] 1.03621880 2.35034944
[134,] 3.24269484 1.03621880
[135,] -0.59812384 3.24269484
[136,] -2.39689076 -0.59812384
[137,] -9.06617600 -2.39689076
[138,] 2.60946687 -9.06617600
[139,] -6.50367131 2.60946687
[140,] -9.09374065 -6.50367131
[141,] -2.04891692 -9.09374065
[142,] -7.64569315 -2.04891692
[143,] -0.42637847 -7.64569315
[144,] 0.16975073 -0.42637847
[145,] -1.78763365 0.16975073
[146,] -2.65360439 -1.78763365
[147,] -2.08519196 -2.65360439
[148,] -4.10978114 -2.08519196
[149,] -0.10036432 -4.10978114
[150,] -1.18576012 -0.10036432
[151,] 0.61724067 -1.18576012
[152,] 5.72622401 0.61724067
[153,] -8.97589966 5.72622401
[154,] -0.92221464 -8.97589966
[155,] -0.44516291 -0.92221464
[156,] 0.36699296 -0.44516291
[157,] 0.10740655 0.36699296
[158,] 2.27782532 0.10740655
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.34990302 1.96198836
2 -0.47541873 -2.34990302
3 -0.12838779 -0.47541873
4 -3.95022967 -0.12838779
5 4.94195085 -3.95022967
6 0.23105658 4.94195085
7 -1.54976023 0.23105658
8 0.04120504 -1.54976023
9 2.55219581 0.04120504
10 3.81651422 2.55219581
11 -1.75907577 3.81651422
12 3.62765168 -1.75907577
13 -5.67208736 3.62765168
14 -1.44582232 -5.67208736
15 3.74280788 -1.44582232
16 -2.17484625 3.74280788
17 -7.02248808 -2.17484625
18 2.54786551 -7.02248808
19 -1.38779014 2.54786551
20 -2.23637049 -1.38779014
21 -0.66062022 -2.23637049
22 1.83163864 -0.66062022
23 1.91876212 1.83163864
24 6.09146220 1.91876212
25 2.12699047 6.09146220
26 0.53857534 2.12699047
27 3.62286332 0.53857534
28 1.02733562 3.62286332
29 0.30929750 1.02733562
30 4.75531461 0.30929750
31 2.00569913 4.75531461
32 -7.01819970 2.00569913
33 -0.96292485 -7.01819970
34 -1.58102251 -0.96292485
35 -0.69534591 -1.58102251
36 -3.11401000 -0.69534591
37 -0.82538213 -3.11401000
38 -0.55175486 -0.82538213
39 -2.59731477 -0.55175486
40 7.10454125 -2.59731477
41 0.33607077 7.10454125
42 -1.71155041 0.33607077
43 -0.78979783 -1.71155041
44 -1.25143295 -0.78979783
45 2.60650322 -1.25143295
46 3.27761284 2.60650322
47 -3.43467660 3.27761284
48 0.03602926 -3.43467660
49 -2.81883590 0.03602926
50 -1.68926801 -2.81883590
51 2.18804543 -1.68926801
52 6.51573122 2.18804543
53 -5.02362508 6.51573122
54 0.20255893 -5.02362508
55 5.59252953 0.20255893
56 3.15714033 5.59252953
57 -1.16303736 3.15714033
58 4.05442013 -1.16303736
59 -4.35252389 4.05442013
60 2.14394489 -4.35252389
61 0.52393199 2.14394489
62 4.35720463 0.52393199
63 -0.22191044 4.35720463
64 0.32737746 -0.22191044
65 3.10779059 0.32737746
66 2.71597942 3.10779059
67 -3.09874711 2.71597942
68 1.73082689 -3.09874711
69 1.08180703 1.73082689
70 1.21796598 1.08180703
71 0.57247890 1.21796598
72 3.17370674 0.57247890
73 1.39778167 3.17370674
74 1.93604025 1.39778167
75 -0.28553632 1.93604025
76 1.35958887 -0.28553632
77 1.85541908 1.35958887
78 5.48789773 1.85541908
79 -1.64752119 5.48789773
80 3.43204374 -1.64752119
81 6.37066954 3.43204374
82 0.69523298 6.37066954
83 1.96733304 0.69523298
84 3.89449444 1.96733304
85 2.64647976 3.89449444
86 -0.85019593 2.64647976
87 4.64491388 -0.85019593
88 -0.14227017 4.64491388
89 0.42734346 -0.14227017
90 3.55489120 0.42734346
91 2.28084508 3.55489120
92 1.05913474 2.28084508
93 -2.30337914 1.05913474
94 5.65161350 -2.30337914
95 2.57537377 5.65161350
96 3.05311094 2.57537377
97 0.93578937 3.05311094
98 0.23214597 0.93578937
99 -1.38337825 0.23214597
100 -0.58158203 -1.38337825
101 -0.27572895 -0.58158203
102 -3.76262253 -0.27572895
103 1.35752171 -3.76262253
104 7.23185808 1.35752171
105 -6.72853005 7.23185808
106 -3.69146833 -6.72853005
107 -4.91072102 -3.69146833
108 -0.64616017 -4.91072102
109 -0.02239783 -0.64616017
110 -0.28461709 -0.02239783
111 5.93891172 -0.28461709
112 -2.24985719 5.93891172
113 -8.08949429 -2.24985719
114 -1.81876407 -8.08949429
115 1.61053238 -1.81876407
116 -8.05627027 1.61053238
117 -3.85247750 -8.05627027
118 1.81847342 -3.85247750
119 -7.42089490 1.81847342
120 -3.36007828 -7.42089490
121 -5.12385967 -3.36007828
122 -4.22847471 -5.12385967
123 2.26085385 -4.22847471
124 0.84498997 2.26085385
125 1.57078145 0.84498997
126 0.44729768 1.57078145
127 2.49698689 0.44729768
128 4.51183125 2.49698689
129 0.86785399 4.51183125
130 -4.29139020 0.86785399
131 1.13145646 -4.29139020
132 2.35034944 1.13145646
133 1.03621880 2.35034944
134 3.24269484 1.03621880
135 -0.59812384 3.24269484
136 -2.39689076 -0.59812384
137 -9.06617600 -2.39689076
138 2.60946687 -9.06617600
139 -6.50367131 2.60946687
140 -9.09374065 -6.50367131
141 -2.04891692 -9.09374065
142 -7.64569315 -2.04891692
143 -0.42637847 -7.64569315
144 0.16975073 -0.42637847
145 -1.78763365 0.16975073
146 -2.65360439 -1.78763365
147 -2.08519196 -2.65360439
148 -4.10978114 -2.08519196
149 -0.10036432 -4.10978114
150 -1.18576012 -0.10036432
151 0.61724067 -1.18576012
152 5.72622401 0.61724067
153 -8.97589966 5.72622401
154 -0.92221464 -8.97589966
155 -0.44516291 -0.92221464
156 0.36699296 -0.44516291
157 0.10740655 0.36699296
158 2.27782532 0.10740655
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7yxk11290537787.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8yxk11290537787.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9yxk11290537787.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1096jm1290537787.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/115zkn1290537788.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/128zjs1290537788.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13fixm1290537788.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14qsx71290537788.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15bavv1290537788.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/1672tm1290537788.tab")
+ }
>
> try(system("convert tmp/1254s1290537787.ps tmp/1254s1290537787.png",intern=TRUE))
character(0)
> try(system("convert tmp/2delv1290537787.ps tmp/2delv1290537787.png",intern=TRUE))
character(0)
> try(system("convert tmp/3delv1290537787.ps tmp/3delv1290537787.png",intern=TRUE))
character(0)
> try(system("convert tmp/4delv1290537787.ps tmp/4delv1290537787.png",intern=TRUE))
character(0)
> try(system("convert tmp/5delv1290537787.ps tmp/5delv1290537787.png",intern=TRUE))
character(0)
> try(system("convert tmp/656ky1290537787.ps tmp/656ky1290537787.png",intern=TRUE))
character(0)
> try(system("convert tmp/7yxk11290537787.ps tmp/7yxk11290537787.png",intern=TRUE))
character(0)
> try(system("convert tmp/8yxk11290537787.ps tmp/8yxk11290537787.png",intern=TRUE))
character(0)
> try(system("convert tmp/9yxk11290537787.ps tmp/9yxk11290537787.png",intern=TRUE))
character(0)
> try(system("convert tmp/1096jm1290537787.ps tmp/1096jm1290537787.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.136 1.735 9.306